Signal processing of stochastic forecast: seasonal electric power consumption in the scope of efficiency connected to renewable energy

2025-03-26T19:53:34+00:00

The electric power system's seasonality would mitigate the problem of the energy volatility crisis. Seasonal identification is important in energy usage analysis to make a better an organization's energy performance. Identification of the seasonal component in a time series data is essential since it defines a fluctuation within an interval of time. This seasonal component can be identified as a periodic phenomenon. Signal processing models can be used in analyzing periodic data in the representation of sinusoidal function. Seasonal variations are an important factor influencing the behavior of an electric power and energy load. This paper introduces the application of deterministic functions in the Ornstein\textendash Uhlenbeck process as a sinusoidal signal processing model in identifying seasonal components represented in periodic terms. We conducted extensive experiments to validate the model on three datasets in electric power and energy systems namely electricity load, household electric power consumption, and power consumption of the three source stations. Further, it can be obtained that the periodic continuous-time-inhomogeneous signal of Ornstein\textendash Uhlenbeck model can be used to identify the seasonal term of the very short to medium horizon forecast of the electric power and energy system. Seasonal changes in energy consumption can be used in energy management in the scope of energy efficiency connected to renewable energy plans.

One- and multi-diode PV module models: PSO-based parameter extraction and performance evaluation under conventional and low-concentration photovoltaic conditions

2026-02-10T20:02:13+00:00

This study investigates the performance of diode-based photovoltaic (PV) modules models by analyzing their effectiveness in predicting the electric behviour under conventional solar irradiation and low-concentration photovoltaic (LCPV) conditions. The parameters of one-diode (1-DM), two-diode (2-DM), three-diode (3-DM) and four-diode models (4-DM) are first extracted using the particle swarm optimization technique (PSO) and validated through a comparative analysis with experimental measurements carried out on a PV module (ISOFOTON 106 W-12 V) in real-world temperature and irradiation conditions of 27.2°C and 755 W/m², respectively. The findings reveal that the 4-DM exhibits the minimum deviation from experimental data in predicting key performance metrics such as short-circuit current (I_sc), open-circuit voltage (V_oc), and maximum output power (P_m). However, this increased accuracy comes at the cost of higher computational complexity in optimizing the 4-DM’s parameters. The studies carried out under several low-concentration photovoltaic conditions show clearly the limitation of the 1-DM in terms of predicted (P_m), efficiency, and fill factor (FF). Indeed, the gaps in the obtained values of efficiency and FF with respect to the 4-DM increase with the concentration ratio and reach 0.74% and 0.04, respectively, at 3 suns. The performances obtained with the 2-DM and 3-DM remain stable and close to those of the 4-DM with constant gaps in the obtained values of efficiency and FF, remaining close to 0.1% and 0.01, respectively, regardless of the concentration ratio. The insights gained from this work underscore the significance of selecting an appropriate PV model for LCPV systems, balancing accuracy and computational efficiency.

Transactions on Energy Systems and Engineering Applications

Signal processing of stochastic forecast: seasonal electric power consumption in the scope of efficiency connected to renewable energy

One- and multi-diode PV module models: PSO-based parameter extraction and performance evaluation under conventional and low-concentration photovoltaic conditions